Instructions to use MGeorgieff/ResNet151V2_GRADCAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MGeorgieff/ResNet151V2_GRADCAM with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MGeorgieff/ResNet151V2_GRADCAM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9fbffd1cd2342be208828aab8d282598f6ef03638d98664e36db8da3a3e34359
- Size of remote file:
- 274 MB
- SHA256:
- 284f5ad8854b31ca4f84eb5b6b1f28646376ee16b013db677b3078127f37f2ed
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